System and method for the creation and the exchange of a copyright for each ai-generated multimedia via a blockchain

ABSTRACT

A method for creating and exchanging a copyright for each artificial intelligence (AI)-generated multimedia is described. An AI model and a reference input for a multimedia is received from a user. If the reference input complies with system policies, an AI-generated multimedia is generated from the reference input using the AI model. The AI-generated multimedia is compared against works of a same type in a blockchain and decentralized file storage and if the AI-generated multimedia fails to match the works, the AI-generated multimedia is categorized as having originality. A copyright for the AI-generated multimedia and the AI-generated multimedia is stored. An exchange is facilitated with a buyer using cryptocurrency and is written to a blockchain.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a U.S. Non-Provisional Patent Application andcontinuation of U.S. Non-Provisional patent application Ser. No.17/211,569 filed on Mar. 24, 2021, the entire contents of which arehereby incorporated by reference in their entirety.

FIELD OF THE EMBODIMENTS

The field of the invention and its embodiments relate to a system and amethod for the creation and the exchange of a copyright for eachAI-generated multimedia via a blockchain.

BACKGROUND OF THE EMBODIMENTS

Artificial intelligence (AI) systems can generate many kinds of digitalmultimedia, including text (such as stories or poems), graphics (such aspictures, photographs, icons, faces, etc.), animations (such astwo-dimensional or three-dimensional motion graphics), video (such asmovies or clips) and sound (such as music, noises, ring bells, etc.) asif they were generated by humans, with little or without humanintervention. Without differentiating in the “creativity” as workscreated by humans, these AI-generated multimedia are worth consideringfor some kinds of copyright protection.

Blockchain technology is an innovative technology for organizing data ina secured manner. More specifically, a blockchain is a distributedledger that can record transactions between two computer systemsefficiently and in a verifiable and permanent way. A typical blockchainis a growing list of records, referred to as blocks, that are linkedusing cryptography. A blockchain database is typically managedautonomously using a peer-to-peer network and a distributed timestampingserver. In many decentralized blockchains, every node in thedecentralized system has a copy of the blockchain. Transactions arebroadcast over the computer network and data quality is maintained bydatabase replication and computational trust.

Many blockchains are publicly accessible and are referred to aspermissionless. In a permissionless blockchain, any computing system canchoose to run a node for the blockchain and participate in transactionverifications via a mining mechanism, as well as create smart contractson the network. In these frameworks, network participants are rewardedfor their contributions through issuance of cryptographic tokens orcryptocurrency.

On the other hand, a permissioned blockchain is a closed computingsystem in which each participant is well-defined. This type ofblockchain is built to allow an organization or a consortium oforganizations to efficiently exchange information and recordtransactions. In a permissioned blockchain, only pre-approved entitiescan run the nodes that validate transaction blocks and execute smartcontracts on the blockchain.

In both permissioned and permissionless blockchains, a smart contract orsoftware programs may run on the blockchain. A smart contract enforces aset of rules using cryptographic code. Smart contracts are usuallydeveloped as high-level programming abstractions that can be compileddown to bytecode, which can be deployed to a blockchain for execution bycomputer systems using a virtual machine deployed in conjunction withthe blockchain. Once a smart contract is called within a blockchain, thecode of the smart contract acts as a programmatically defined autonomousagent with its own persistent variables that executes by computersystems within the blockchain when the smart contract is referenced by amessage and/or transaction. The output of the smart contract (such asupdated wallet balance, update of information, etc.) is then written toblockchain for permanent recording.

As such, blockchains are a natural choice for copyright management ofAI-generated multimedia. An AI process that is to generate multimediacan be coded into a smart contract and the copyright can be recorded inthe blockchain. The generated multimedia become cryptographic tokensthat the ownership is associated with them as copyrights. Cryptographictokens or non-fungible tokens (NFTs) are created with respect to theAI-generated multimedia and may be used to transfer the AI-generatedmultimedia via smart contracts. If the two processes, the creation ofmultimedia and the recording of associated copyright, are integratedwithin a blockchain system, it helps securely verify the origin of themultimedia, which supports the evidence of copyright issuance and makeit possible to exchange the copyright in a trusted manner. Onceseparated, there will appear numerous limitations.

First of all, once the creation happens outside of the blockchain system(i.e., not via the specific smart contract of the blockchain, or not viaa compatible smart contract to the blockchain), there can't be anyrecord to verify its origin (who and when and how). If the origin of themultimedia can't be justified, its associated copyright is meaningless.Conventional NFT systems are limited by this origin verification.

Secondly, when a digital multimedia is generated, it is not only the onewho uses the tool to create it, but it's necessary to recognize thecontribution of the programmer who created the tool, the provider whomade the tool available. All involved people should be considered to berelated to the creation and should be able to receive royalties from anyprofit of the exchanging of the multimedia may arise.

Furthermore, from a technical viewpoint, blockchain enterpriseapplications are difficult to implement since they require knowledge ofcryptography, peer-to-peer systems, and specialized languages used inblockchain smart contracts. Other technical issues associated withblockchains include interfacing an application on the blockchain toalready existing technologies, such as reporting services and databases.

Thus, a need exists for a method in which the blockchain is equippedwith the functionalities from AI to be able to become both a validatorand an executor of the data that it will preserve. Moreover, a needexists for a system and a method for the creation and the exchange of acopyright for each AI-generated multimedia via a blockchain.

Review of related technology:

KR1020200012321A describes a blockchain-based intellectual propertyrights management system that can build an ecosystem of intellectualproperty rights to induce a desirable registration and transaction ofcopyrights, publicity rights, and industrial property rights consistingof patents, utility models, trademarks, and designs. When events relatedto intellectual property rights registration, intellectual propertyrights transaction, digital virtual currency deposit, intellectualproperty rights contract, and intellectual property rights payment aregenerated, blocks are created from the events to transfer theinformation events to a plurality of blockchain-based user terminals anda blockchain-based service company server. At least one operation forgenerating a blockchain by connecting the blocks to the previous blocksis executed.

US20200160465A1 describes a comprehensive platform (the “IPwe Platform”)that utilizes blockchain and smart contracts to address and improve uponthe significant deficiencies that currently exist in the globalintellectual property market (patents, trademarks, copyrights, etc.).The objective of the IPwe Platform is creating transparency in (i)patent ownership, (ii) patent identification and (iii) patent coverageand value. In addition, by providing the network, a. guaranteed buyprogram can be initiated that provides specific guaranteed value tointellectual property assets listed by verified brokers.

US20200111186A1 describes a system configured to manage and trackintellectual property (IP). Some embodiments of this system may include:an application programming interface (API) having access to aweb-accessible IP database; an artificial intelligence (AI) or learningengine coupled to the API and operating the API to query theweb-accessible IP database for orphaned IP assets that have becomeabandoned, expired, and/or public domain assets; and auser-collaboration platform. Some embodiments of this platform may:receive information from the API concerning the orphaned IP assets foundby the API; store the information concerning each of the orphaned IPassets on a blockchain data structure; provide access to the orphaned IPassets stored on the blockchain data structure to users via a network;and record user activity concerning user access, development, sharing,and/or modification of the orphaned IP assets on the blockchain datastructure.

U.S. Pat. No. 10,592,639B2 describes use of blockchain-based shadowimages to facilitate copyright protection of digital content.Specifically, this reference describes a client platform that supportsdigital rights management. The client platform comprises a digitalrights management (DRM) engine which, when executed, enables the clientplatform to monitor download operations performed by the client platformand to obtain a shadow image for a digital content item from a DRMblockchain, in response to an operation to download the digital contentitem from a remote source. The shadow image comprises a hash of thedigital content item and copyright policy settings to indicate securityconstraints for the digital content item. The client platform mayautomatically determine whether the copyright policy settings for thedigital content item allow modification of the digital content item.

US20210044439A1 describes a method and system of storing a record of acopyright event in a blockchain through an agent. Devices of some workservice providers operating work-related services can serve as membernodes to form a consortium blockchain network. Each work serviceprovider broadcasts copyright events generated based on its work-relatedservices to the consortium blockchain network, so all the work serviceproviders perform blockchain-based distributed storage. In addition, theplurality of member nodes include at least one agenting member node,where each agenting member node has a right to agent copyright eventrecord storage for a non-member node corresponding to the agentingmember node.

EP3780544A1 describes a method and system of storing a record of acopyright event based on a blockchain. Devices of some work serviceproviders operating work-related services can serve as member nodes toform a consortium blockchain network. Each work service providerbroadcasts copyright events generated based on its work-related servicesto the consortium blockchain network, so all the work service providersperform blockchain-based distributed storage.

CN107659610B describes a copyright protection method, device, and systembased on blockchain technology. The method comprises: obtainingcopyright information of works; packaging and writing the copyrightinformation in a blockchain; issuing the blockchain to a network;receiving an authorization request sent by a requester; extractingcorresponding copyright information from the blockchain according to theauthorization request; forming authorization information based on thecopyright information; feeding back the authorization information to therequester; packaging and writing the authorization information in theblockchain; and issuing the blockchain to the network.

US20200372834A1 describes methods and systems for hiding copyrightinformation in printable materials. One of the methods includes:generating, by a computing device, a unique identifier (ID) based oncopyright information associated with digital content, where thecopyright information and the digital content are recorded on ablockchain of a blockchain network; determining one or more featuresassociated with one or more printable materials; converting the uniqueID to a digital watermark based on the one or more features, where thedigital watermark is not apparent to an unaided human eye when printedon the one or more printable materials; and enabling retrieval of thecopyright information from the blockchain based on the unique ID.

Various similar systems exist. However, their means of operation aresubstantially different from the present disclosure, as the otherinventions fail to solve all the problems taught by the presentdisclosure.

SUMMARY OF THE EMBODIMENTS

The present invention and its embodiments relate to a system and amethod for the creation and the exchange of a copyright for eachAI-generated multimedia via a blockchain.

A first embodiment of the instant invention describes a systemconfigured to execute a method for creating and exchanging a copyrightfor each artificial intelligence (AI)-generated multimedia. The systemincludes, at least, a multimedia generation module, a copyright claimingmodule, and an asset exchanging module. The multimedia generation moduleis configured to: receive a user selection of an AI model for amultimedia and receive a reference input for the multimedia from theuser. In response to a determination that the reference input complieswith system policies (e,g., rules associated with restricted content,child endangerment, inappropriate content, sexual content, profanity,hate speech, violence, terrorist, bullying, harassment, and/or dangerousproducts), the multimedia generation module is configured to: generatean AI-generated multimedia from the reference input using the AI model.The determination that the reference input complies with the systempolicies occurs automatically using the AI model and/or using humanintervention. In some examples, the multimedia generation module alsocomprises a legal component configured to compute a legal correctnessfor the reference input according to regulations and copyright laws.

Next, the copyright claiming module is configured to: receive anindication from the user that the user wants to claim a copyright in theAI-generated multimedia. Then, the copyright claiming module isconfigured to: verify the originality of the AI-generated multimedia bycomparing the AI-generated multimedia against works of a same type thatalready exist in the blockchain. In response to a determination that theAI-generated multimedia fails to match the works of the same type in thestorage, the copyright claiming module is configured to: identify theAI-generated multimedia as having originality. Moreover, the copyrightclaiming module is configured to: set the copyright to associate withthe AI-generated multimedia and store the information into theblockchain and store the AI-generated multimedia in a decentralized filestorage. In other examples, the copyright claiming module is furtherconfigured to: utilize the information which are stored in the file andblockchain storage to verify the originality of the AI-generatedmultimedia; and allow the user to edit some accessory informationrelated to the AI-generated multimedia and write those information tothe blockchain (for examples: a title of the multimedia, a privatemessage, an unique identification number)

In other examples, in response to a determination that the AI-generatedmultimedia. matches the works of the same type in the storage, thecopyright claiming module is configured to identify the AI-generatedmultimedia as lacking the originality. Then, the claiming module isfurther configured to: receive a notification from the user that theuser wishes to wait for a future update of a verification policy byqueueing the AI-generated multimedia; and store a pending copyright forthe AI-generated multimedia in the storage.

The asset exchanging module is configured to: receive a request from abuyer to use the copyright for the AI-generated multimedia, prompt theuser to exchange the copyright for the AI-generated multimedia with thebuyer for cryptocurrency, facilitate the exchange between the user andthe buyer, and write the exchange to the blockchain. In examples, thecryptocurrency may be non-fungible tokens (NFTs) or cryptographictokens. The exchange of the copyright includes the permission to usewith limited approval (such as display only, replaying in a number oftimes, etc.), or permission to adapt, or totally transferring of thecopyright.

A second embodiment of the present invention describes a method. Themethod is executed by a system for method for creating and exchanging acopyright for each artificial intelligence (AI)-generated multimedia.The method includes numerous process steps, such as: receiving, via amultimedia generation module of the system, a user selection of an AImodel for a multimedia, receiving, via the multimedia generation module,a reference input for the multimedia from the user; and in response to adetermination that the reference input complies with system policies,generating, via the multimedia generation module, an AI-generatedmultimedia from the reference input using the AI model. Determining ifthe reference input complies with system policies occurs automaticallyusing the AI model and/or using human intervention.

The method also includes: receiving, via a copyright claiming module ofthe system, a notification from the user that the user wants to claim acopyright in the AI-generated multimedia; comparing, via the copyrightclaiming module, the AI-generated multimedia against works of a sametype in a storage; in response to a determination that the AI-generatedmultimedia fails to match the works of the same type in the storage,identifying, via the copyright claiming module, the AI-generatedmultimedia as having originality; and storing, via the copyrightclaiming module, the copyright for the AI-generated multimedia and theAI-generated multimedia in the storage.

In response to a determination that the AI-generated multimedia matchesthe works of the same type in the storage, the method further comprises:identifying, via the copyright claiming module, the AI-generatedmultimedia as lacking the originality; receiving, via the copyrightclaiming module, a notification from the user that the user wishes waitfor a future update of a verification policy by queueing theAI-generated multimedia; and storing, via the copyright claiming module,a pending copyright for the AI-generated multimedia in the storage.

The method may further include: utilizing a first smart contract forexecution of the AI model; and utilizing a second smart contract toverify the originality of the AI-generated multimedia. The verificationpolicy can be based on an AI model, or crowd voting or a humanvalidator, blind voting or aggregation of those. The selection of thispolicy or other policies can be done in a decentralized autonomousorganization (DAO) manner or in a centralized manner. In a DAO manner,the community organizes voting for selection of a policy. In acentralized manner, an authorized party makes the decision on thepolicy.

Moreover, the method further includes: utilizing, via the smart contractof the copyright claiming module, the information which are stored inthe file and blockchain storage to verify the originality of theAI-generated multimedia; and allow the user to edit some accessoryinformation related to the AI-generated multimedia and write thoseinformation to the blockchain (for examples: a title of the multimedia,a private message, an unique identification number).

The method further includes: receiving, via an asset exchanging moduleof the system, a request from a buyer to use the copyright for theAI-generated multimedia; prompting, via the asset exchanging module, theuser to exchange the copyright for the AI-generated multimedia with thebuyer for a payment; facilitating, via the asset exchanging module, theexchange between the user and the buyer; and writing, via the assetexchanging module, the exchange to the blockchain. The payment may besplit evenly between the user, an AI programmer, and an AI API provider.In other examples, the payment is split disproportionally between theuser, an AI programmer, and an AI API provider.

In general, the present invention succeeds in conferring the followingbenefits and objectives.

It is an objective of the present invention to provide a system and amethod for the creation and the exchange of a copyright for eachAI-generated multimedia via a blockchain.

It is an objective of the present invention to provide a method and asystem for automatically claiming a copyright for AI-generatedmultimedia.

It is an objective of the present invention to provide an automated andorganized system of copyright issuing.

It is an objective of the present invention to provide a verificationmethod utilized with a system that automatically claims a copyright forAI-generated multimedia using blockchain technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a schematic diagram of a system configured to create andexchange a copyright for each AI-generated multimedia via a blockchain,according to at least some embodiments disclosed herein.

FIG. 2 depicts another schematic diagram of a system configured tocreate and exchange a copyright for each AI-generated multimedia via ablockchain, according to at least some embodiments disclosed herein.

FIG. 3 depicts a block diagram of components of a platform configured tocreate and exchange a copyright for each AI-generated multimedia via ablockchain, according to at least some embodiments disclosed herein.

FIG. 4 depicts a flowchart for a method executed by a system configuredto create and exchange a copyright for each AI-generated multimedia viaa blockchain, according to at least some embodiments disclosed herein.

FIG. 5A depicts a system configured to create and exchange a copyrightfor each AI-generated multimedia via a blockchain, according to at leastsome embodiments disclosed herein.

FIG. 5B depicts another system configured to create and exchange acopyright for each AI-generated multimedia via a blockchain, accordingto at least some embodiments disclosed herein.

FIG. 6 depicts a schematic diagram of originality verification executedby a system, according to at least some embodiments disclosed herein.

FIG. 7 depicts a schematic diagram showcasing a mechanism for conflicthandling with legal checking executed by a system, according to at leastsome embodiments disclosed herein.

FIG. 8 depicts a schematic diagram showcasing currency flow whencreating AI-generated multimedia, according to at least some embodimentsdisclosed herein.

FIG. 9 depicts a schematic diagram of currency flow when exchangingAI-generated multimedia, according to at least some embodimentsdisclosed herein.

FIG. 10 depicts a block diagram depicting how a system provides anAppstore for applications, according to at least some embodimentsdisclosed herein.

FIG. 11 depicts a block diagram of a server which may be used by asystem, according to at least some embodiments disclosed herein.

FIG. 12 depicts a block diagram of a client device which may be used bya system, according to at least some embodiments disclosed herein.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The preferred embodiments of the present invention will now be describedwith reference to the drawings. Identical elements in the variousfigures are identified with the same reference numerals.

Reference will now be made in detail to each embodiment of the presentinvention. Such embodiments are provided by way of explanation of thepresent invention, which is not intended to be limited thereto. In fact,those of ordinary skill in the art may appreciate upon reading thepresent specification and viewing the present drawings that variousmodifications and variations can be made thereto.

As used herein, the term “computer” refers to a machine, apparatus, ordevice that is capable of accepting and performing logic operations fromsoftware code. The “application”, “software”, “software code” or“computer software” refers to any set of instructions operable to causea computer o perform an operation. Software code may be operated on byan “engine” or processor. Thus, the methods and systems of the presentinvention may be performed by a computer or computing device having aprocessor based on instructions received by computer applications andsoftware.

The term “electronic device,” “mobile device,” or “client device,” asused herein is a type of computer comprising circuitry and configured togenerally perform functions such as recording audio, photos, videos andhandwritten notes; displaying or reproducing audio, photos, videos andhandwritten notes; storing; retrieving, or manipulation of electronicdata; providing electrical communications and network connectivity; orany other similar function. Non-limiting examples of electronic devicesinclude: personal computers (PCs), workstations, laptops, tablet PCs,cell phones, digital music players, digital notepads, digital pens orany electronic device capable of running computer software anddisplaying information to a user, memory cards, other memory storagedevices, digital cameras, external battery packs, external chargingdevices, and the like. Certain types of electronic devices which areportable and easily carried by a person from one location to another maysometimes be referred to as a “portable electronic device” or “portabledevice”. Some non-limiting examples of portable devices include: cellphones, smartphones, tablet computers, laptop computers, wearablecomputers such as smartwatches, fitness rings, fitness trackers, etc.

The term “computer readable medium” as used herein refers to any mediumthat participates in providing instructions to the processor forexecution. A computer readable medium may take many forms, including butnot limited to, non-volatile media, volatile media, and transmissionmedia. Non-volatile media includes, for example, optical, magneticdisks, and magneto-optical disks, such as the hard disk or the removablemedia drive. Volatile media includes dynamic memory, such as the mainmemory. Transmission media includes coaxial cables, copper wire andfiber optics, including the wires that make up the bus. Transmissionmedia may also take the form of acoustic or light waves, such as thosegenerated during radio wave and infrared data communications.

As used herein the term “data network” or “network” shall mean aninfrastructure capable of connecting two or more computers such asclient devices either using wires or wirelessly allowing them totransmit and receive data. Non-limiting examples of data networks mayinclude the internet or wireless networks or (i.e. a “wireless network”)which may include WM and cellular networks. For example, a network mayinclude a local area network (LAN), a wide area network (WAN) (e.g., theInternet), a mobile relay network, a metropolitan area network (MAN), anad hoc network, a telephone network (e.g., a Public Switched TelephoneNetwork (PSTN)), a cellular network, a Zigby network, or a voice-over-IP(VoIP) network.

As used herein, the term “database” shall generally mean a digitalcollection of data or information. A database may be stored on a remoteserver and accessed by a client device through the Internet (e.g., thedatabase is in the cloud) or alternatively in some embodiments thedatabase may be stored on the client device or remote computer itself(e.g., local storage). A “data store” as used herein may contain orcomprise a database (e.g., information and data from a database may berecorded into a medium on a data store).

Blockchains

As described herein, the term “blockchain” refers to a distributeddatabase that maintains a continuously growing ledger or list ofrecords, called “blocks,” secured from tampering and revision usinghashes. Every time data is published to a blockchain database, the datamay be published as a new block. Each block may include a timestamp anda link to a previous block. Through the use of a peer-to-peer networkand a distributed timestamping server, a blockchain database is managedautonomously.

Permissionless blockchains are an open, distributed ledger that canrecord transactions between two parties efficiently and in a verifiableand permanent way. It should be appreciated that permissionedblockchains are also targeted with this present invention. Consensusensures that the shared ledgers are exact copies, which lowers the riskof fraudulent transactions. Cryptographic hashes, such as the SHA256computational algorithm, ensure that any alteration to transaction inputresults in a different hash value being computed, which indicates apotentially compromised transaction input. Digital signatures ensurethat transactions originated from senders (signed with private keys) andnot imposters. This covers different approaches to the processing,including hash trees and hash graphs. At its core, a blockchain systemrecords the chronological order of transactions with all nodes agreeingto the validity of transactions using the chosen consensus model. As aresult, transactions are irreversible and agreed to by all members inthe network.

An example of a blockchain is a cryptocurrency. The cryptocurrency isgenerated when new blocks are created on the blockchain to confirmtransactions of the cryptocurrency. The new blocks may confirm thetransfer of cryptocurrency generated in earlier blocks. The blocks onthe blockchains are cryptographically proofed and linked to earlierblocks and served as an immutable record of the events in a trustlessdecentralized peer-to-peer network.

For example, a cryptocurrency (e.g., Bitcoin) is represented as a chainof events that transfers ownership from one party to another party on ablockchain without an intermediary. Each event transferring ownershipfrom one party to another is cryptographically proofed by including thepublic key of the new owner. Further, each event is digitally signedwith the current owner's private key.

A new block in a blockchain is filled with cryptographically proofedevents until the block reaches a specified size limit. A hash digest ofall the event identifiers within the block and the block header of theprevious block are added as the first event in the block. Each block ofevents may be secured by a race between participants on a peer-to-peernetwork. In order to win the race the participants collect new events tocreate the new block, validate the events on the new block by verifyingthe cryptographic proofs of each event to verify the cryptocurrency wasnot spent earlier, and solve a mathematical puzzle based on the hashdigest, previous block header and a random number. Blockchain provides amathematical hierarchy of verifiable events that is immutable and isverified at each stage by the race between the participants.

Smart Contracts

The principles used in blockchains may be modified to allow forexecution of smart contracts deployed on the blockchain. As definedherein, “smart contracts” are self-executing machine-readableinstructions that store state information and are stored on theblockchain. When deployed, the smart contract is assigned a uniqueaddress to allow communication to and from the smart contract throughmessages. The smart contract is deployed by storing the smart contractas an event on the blockchain e.g., (Ethereum™ blockchain). Messages tothe smart contract may be posted as events on the blockchain. The smartcontract may contain machine-readable instructions and data designed toexecute on virtual machines.

Further, the smart contract may have the ability to read or write to itsinternal storage storing data, read the storage of a received message,and/or send messages to other smart contracts to trigger execution ofthe code in other distributed applications. When the smart contract isexecuted on a virtual machine running on the peers securing theblockchain, the resulting data may be saved in the internal storage ofthe smart contract. The updated smart contract may be stored as an eventon a new block. Thus, the smart contract and changes to data arerepresented as a series of events on the blockchain. Similar to thecryptocurrency blockchain, each block in the blockchain by mining theblockchain by peers based on a consensus protocol.

For example, in a smart contract that governs a sale of an electronicasset, the smart contract may include machine-readable instructions toaccess its internal storage, to read the storage of a message sent tothe smart contract and to process the data in a received message, suchas a counter-offer from a buyer. When the buyer sends a counter-offer tothe smart contract, the smart contract may update its internal storageto include the counter-offer event, such as the identity of the buyer.The updated smart contract may be recorded as an event (e.g., atransaction) on a new block on the blockchain. As such, the blockchainstores the changes in the state of the smart contract as a series ofevents (e.g. a transaction).

Cryptographic Digital Assets

As explained, the smart contract governs a sale of an electronic asset,a “cryptographic digital asset,” or a “digital asset,” which refers toany computer-generated virtual object, including digital apparel,avatars, pets, art, etc., that have a unique, non-fungible tokenizedcode (“NFT”) registered on and validated by a blockchain platform orregistered in an immutable database. Specifically, NFT's describeblockchain-based cryptographic tokens that are created with respect to aspecific piece of content, which incorporate programmatically defineddigital rights management. The metadata associated with an NFT may alsoinclude digital media assets such as, but not limited to, images, videosabout the specific NFT or the context in which it was created (studio,film, band, company song etc.). In a number of embodiments, contentcreators can issue NFTs to users within the platform.

In many instances, each NFT has a unique serial number and the NFT smartcontract defines an interface that enables the NFT to be managed, ownedand/or traded. Standards for defining interfaces for building NFTs onthe Ethereum blockchain include: ERC-721 and/or ERC-1155, among others,the disclosures of which are incorporated by reference in theirentirety. NFTs can be contrasted with interchangeable or fungible tokens(e.g. Ether). Fungible tokens can be implemented on the Ethereumblockchain based upon standard interfaces, such as the ERC-20 standard,the disclosure of which is incorporated by reference in its entirety.

In a number of embodiments, the smart contracts defining NFTs that canbe minted within platforms specify fee distribution obligations withrespect to specific types of transactions involving NFTs. In severalembodiments, the sale of an NFT within a platform can result in one ormore residual royalty payment transactions that are recorded in theblockchain, such as: a residual royalty payment to the content creatorthat minted the NFT, an AI programmer, and/or an AI API provider.

In some examples, the authenticity of a particular NFT can be verifiedindependently of the content creator by auditing transaction recordsassociated with the NFT within the blockchain to confirm consistencywith the smart contract underlying the NFT. For example, the presence oftransactions reflecting residual royalty payments that a smart contractindicates should have occurred upon transfers of the NFT can be reliedupon to verify the authenticity of the NFT. Moreover, in embodiments,“wallet applications” enable users to securely store NFTs and/or othertokens on their devices.

Intellectual Property

Intellectual property serves as the foundation of innovation in oureconomy, where government-granted rights incentivize discovery andcreativity by providing creators with an opportunity to profit from thevalue of their innovative work. In exchange, the creative work is, aftera given time period, made public so that others may build on and benefitfrom the work of the original creator. Laws protecting intellectualproperty reduce the transaction costs between inventors and industry byproviding information about the quality of the invention withoutjeopardizing the ownership of the idea.

Generally, four types of intellectual property exist to protect aninnovative idea or invention, which include: (a) a trade secret, (b) atrademark, (c) a copyright, and (d) a patent. Of particular interest, acopyright protects original works of authorship, including literary,dramatic, musical, and artistic works, such as poetry, novels, movies,songs, computer software, and architecture.

Artificial Intelligence

Artificial intelligence (Al) is a field of computer science involvedwith developing a computer's capacity to behave as an intelligententity. See, Jessica L. Gillotte, “Copyright Infringement inAI-Generated Artworks,” University of California. Davis Law Review,2020, 53(2655), Pages 2655-2691 and Atilla Kasap, “Copyright andCreative Artificial Intelligence (AI) Systems: A Twenty-First CenturyApproach to Authorship of AI-Generated Works in the United States,” WakeForest Intellectual Property Law Journal, 2019, 19(4), Pages 335-350,the entire contents of which are hereby incorporated by reference intheir entirety. There are numerous sub-fields of AI research, such asmachine learning. Generally, machine learning is a process by which AIextrapolates patterns from large quantities of data and uses thosepatterns to learn the constraints of the output it is expected toproduce without being explicitly programmed to produce it. Duringmachine learning, the AI program receives feedback and refines itsunderlying algorithm to improve its performance of the task over time.An AI program can learn by receiving feedback from two alternativemethods of training: supervised learning and unsupervised learning.

AI has been used to generate art for almost fifty years. See, Jessica L.Gillotte, “Copyright Infringement in AI-Generated Artworks,” Universityof California Davis Law Review, 2020, 53(2655), Pages 2655-2691, theentire contents of which are hereby incorporated by reference in theirentirety. In fact, one of the earliest uses of AI to create art was acomputer system created by Harold Cohen in 1973 called “AARON.” HaroldCohen taught AARON to draw the way an adult might teach a child to draw,for example, by teaching a child to enclose line scribbles in a closedform. See generally, Arthur R. Miller, “Copyright Protection forComputer Programs, Databases, and Computer-Generated Works: Is AnythingNew Since CONTU?,” 106 Harv. L. Rev. 977, 1047 (1993), the entirecontents of which are hereby incorporated by reference in theirentirety.

In fact, a complex neural network—Generative Adversarial Network(GAN)—is commonly used to create AI-generated artwork. GANs take a gametheoretical approach to machine learning by making use of twosimultaneously trained networks that are tasked with outperforming eachother. The first network, or the generative model, begins with a sampleof random data and generates a random output image. Since the data usedto generate the image is random, the first several images created by theGAN's generative model will appear crude and shapeless. Then, the secondnetwork, or the discriminative model, tries to determine whether thegenerative model's output image is generated or real. Both networks aretrained via backpropagation, and, as the generative model anddiscriminative model try to outmaneuver one another, the overallperformance of the GAN improves. Thus, over time, the GAN's generativemodel creates images that are more difficult to distinguish from thereal ones. Over time, the generated output images are no longerdistinguishable from the real images. See, Jessica L. Gillotte,“Copyright Infringement in AI-Generated Artworks,” University ofCalifornia Davis Law Review, 2020, 53(2655), Pages 2655-2691, the entirecontents of which are hereby incorporated by reference in theirentirety. Other ways to generate the AI-generated multimedia besidesGANs include generative models using machine learning or deep learning.In other implementations, signal processing or image processingalgorithms can be used to generate such AI-generated multimedia. Itshould be appreciated that these examples are provided for illustrativepurposes only and other examples are contemplated.

The instant invention provides a method and system for automaticallygenerating and claiming a copyright for AI-generated multimedia. Itshould be appreciated that AI-generated multimedia may be created in anyway known to one having ordinary skill in the art, such as through useof the GAN discussed herein. The core purpose of copyright law, asstated expressly, is “to promote the progress of science and usefularts,” or in other words, to promote the progress of knowledge andlearning. As such, AI-generated multimedia entails progresses of scienceon themselves and are, therefore, targeted for a kind of copyrightprotection.

Invention

FIG. 1 depicts a schematic diagram of a system configured to create andexchange a copyright for each AI-generated multimedia via a blockchain,according to at least some embodiments disclosed herein. As describedherein, “exchange” means trading, renting, loaning, or lending.

A system of FIG. 1 is configured to facilitate the transfer of data andinformation between one or more access points 106, 110, one or moreclient devices 104A, 104B, and 104C, and one or more servers 128 over adata network 116. It should be appreciated that the quantity of the oneor more client devices 104A, 104B, and 104C is not limited to anyparticular quantity. Moreover, it should be appreciated that a firstclient device 104A of the one or more client devices 104A, 104B, and104C is associated with a first user 102A, a second client device 104Bof the one or more client devices 104A, 104B, and 104C is associatedwith a second user 102B, and a third client device 102C of the one ormore client devices 104A, 104B, and 104C is associated with a third user102C, respectively.

Each of the one or more client devices 104A, 104B, and 104C may be amobile device, a laptop, a tablet computer, a smart phone, a personaldigital assistant, etc., that is equipped with a wireless networkinterface capable of sending data to the one or more servers 128 withaccess to one or more data stores 108 over the data network 116, such asa wireless local area network (WLAN). Additionally, in otherembodiments, each of the one or more client devices 104A, 104B, and 104Cmay be physical fixed devices that are equipped with a wireless or wirednetwork interface capable of sending data to the one or more servers 128with access to the one or more data stores 108 over a wireless or wiredlocal area network.

The present invention may be implemented on at least one client deviceof the one or more client devices 104A, 104B, and 104C and/or at leastone server of the one or more servers 128 programmed to perform one ormore of the steps described herein. In some embodiments, more than oneof the one or more client devices 104A, 104B, and 104C and/or the one ormore servers 128 may be used, with each being programmed to carry outone or more steps of a method or process described herein. Each of theone or more client devices 104A, 104B, and 104C may send data to andreceive data from the data network 116 through a network connection 130with an access point of the one or more access points 106, 110. The oneor more data stores 108 may contain one or more databases (such as adistributed blockchain database and/or a ledger blockchain database,among others).

The system 100 also includes a blockchain network 112 that comprises oneor more nodes 114, which may be in communication with one or moreservers 128, and/or the one or more client devices 104A, 1043, and 104C.A node of the one or more nodes 114 may be a server of the one or moreservers 128, a client device of the one or more client devices 104A,104B, and 104C, or any other suitable networked computing platform. Theblockchain network 112 may manage a distributed blockchain database thatcontains data recorded by the system 100. This data may be maintained asa continuously growing ledger or listing, which may be referred to asblocks, secured from tampering and revision. Each block includes atimestamp and a link to a previous block.

Through the use of a peer-to-peer blockchain network 112 and adistributed timestamping server of the one or more servers 128, a ledgerblockchain database may be managed autonomously. Consensus ensures thatthe shared ledgers are exact copies, and lowers the risk of fraudulenttransactions. Cryptographic hashes are used to ensure that anyalteration to transaction data input results in a different hash valuebeing computed. Further, digital signatures ensure that data entrytransactions (e.g., data added to the ledger blockchain database)originated from senders (signed with private keys). Further, the ledgerblockchain database may record the chronological order of data entrytransactions with the one or more nodes 114 agreeing to the validity ofentry transactions using the chosen consensus model. The result is dataentry transactions that are irreversible and agreed to by all members inthe blockchain network 112.

Moreover, the blockchain network 112 may comprise a cryptocurrency ordigital asset designed to work as a medium of exchange that usescryptography to: secure its transactions, to control the creation ofadditional units, and to verify the transfer of assets. Examplecryptocurrencies include Bitcoin, Etherium, Ripple, etc. The blockchainnetwork 112 may also comprise tokens common to cryptocurrency basedblockchain networks 112.

FIG. 2 depicts another schematic diagram of a system configured tocreate and exchange a copyright for each AI-generated multimedia via ablockchain, according to at least some embodiments disclosed herein.FIG. 3 depicts a block diagram of components of a platform configured tocreate and exchange a copyright for each AI-generated multimedia via ablockchain, according to at least some embodiments disclosed herein.

A platform, as shown in FIG. 3, utilizes one or more immutable ledgers136 (e.g., one or more blockchains) to enable a number of verifiedcontent owners (e.g., the first user 102A) to access an NFT service tomint NFTs 134 in numerous forms, such as: proof of ownership of tangiblecollectibles, proof of ownership of AI-generated multimedia, etc. Inaddition, a smart contract 138 underlying the digital tickets canrequire residual payments when the NFTs 134 are transferred on asecondary market.

Issuance of the NFTs 134 via the platform enables verification of theauthenticity of the NFTs 134 independently of the content owner byconfirming that transactions written to one or more of the immutableledgers 102 are consistent with the smart contracts 108 underlying theNFTs. In examples, the smart contracts 138 underlying the NFTs 134 maycause payments of residual royalties when users engage in specifictransactions involving the NFTs 134 (e.g., transfer of ownership of theNFT 134). In examples, the first user 102A, the second user 102B, and/orthe third user 102C may utilize wallet applications 132 on their devices(e.g., the one or more client devices 104A, 104B, and 104C) to store theNFTs 134 distributed using the platform.

The NFTs 134 that are implemented using the smart contract 138 compriseinterfaces that comply with open standards and are not limited to beingstored within wallets. Furthermore, the instant invention supportsmoving the NFTs 134 between different immutable ledgers 136. Inexamples, when the wallet application 132 is installed upon a userdevice (e.g., the one or more client devices 104A, 104B, and 104C), thewallet application 132 collects data. In some examples, the data 140 iswritten to the immutable ledger 136 that is configured as a permissionedblockchain. In some examples, the manner in which the data 140 iswritten to the immutable ledger 136 enables the wallet application 132to grant permissions with respect to access of the data 140.

The first user 102A, the second user 102B, and/or the third user 102Cmay determine the manner in which the data 140 is accessed and by whom.In many instances, the first user 102A, the second user 102B, and/or thethird user 102C can also revoke access to the data 140 stored within theimmutable ledger 136 using the wallet application 132. In furtherexamples, content creators (e.g., the first user 102A, the second user102B, and/or the third user 102C) can incentivize another user to grantaccess to the data 140 within the immutable ledger 136 using offers oftokens 142 and/or the NFTS 134.

FIG. 4 depicts a flowchart for a method executed by a system configuredto create and exchange a copyright for each AI-generated multimedia viaa blockchain, according to at least some embodiments disclosed herein.

As shown in FIG. 4, the system configured to execute the method forcreating and exchanging the copyright for each AI-generated multimediamay include three modules: a multimedia generation module 202, acopyright claiming module 220, and an asset exchanging module 254. Itshould be appreciated that other modules may be used within this systemthat are not explicitly described herein.

The method of FIG. 4 begins at a process step 204, where the multimediageneration module 202 receives a user selection of an AI model from alist of AI models for many types of multimedia generation (e.g., text,graphics, animations, videos, and/or sound, etc.). Each AI model of theAI modules is associated with a first identifier associated with theprogrammer (e.g., who coded the model or trained the model) and a secondidentifier of the provider (e.g., who made the AI model available forusage). Examples of the AI models include image generation models (suchas CycleGAN or Pix2Pix), music generation models (such as Remi orMagenta), poem generation models, text generation models, animationgeneration models, gesture animation models from videos, and manyothers. It should be appreciated that these examples are provided forillustrative purposes only and other example AI models are contemplatedby the Applicant.

It should be appreciated that the user (e.g., the first user 102A) andan AI programmer 346 use the AI model to generate the data. An AI APIprovider 348 makes the AI model available for the first user 102A. Allof these parties are considered to be related to the discovery andcreation of the AI-generated multimedia and should receive royaltiesfrom any profit resulting from exchanging the data, as will be discussedin turn.

Next, an optional process step 206 may follow the process step 204 thatincludes the multimedia generation module 202 receiving an input from auser of a reference associated with the multimedia. Such reference mayinclude a photograph, text (e.g., a text input for a category a orlabel, such as “dog”, “cat”, etc.), video (e.g., from videos to make anew animation), music (e.g., a reference to Mozart to make a new pieceof music), etc. It should be appreciated that these references areprovided for illustrative purposes only and other references arecontemplated. In this example, in response to receiving the photographof a painting, the AI model may generate the painting to create theAI-generated multimedia.

Then, a process step 208 occurs where the multimedia generation module202 examines the reference input. Such examination may involvedetermining if the reference input is appropriate in view of systempolicies. The system policies may include rules associated withrestricted content, child endangerment, inappropriate content, sexualcontent, profanity, hate speech, violence, terrorist, bullying,harassment, dangerous products, etc. In a first example, the processstep 208 may occur as an automatic method using the AI model. In asecond example, the process step 208 may occur using human intervention.Such examination may also involve referencing one or more databases toperform a legal check for the reference input.

A process step 210 follows the process step 208 and includes themultimedia generation module 202 determining if the reference inputpasses the examination of the process step 208. A “YES” response 212 ora “NO” response 214 may follow the process step 210. If the “YES”response 212 occurs, a process step 216 then occurs, where themultimedia generation module 202 executes the AI model to generate theAI-generated multimedia. Such AI-generated multimedia is outputted at aprocess step 218. Subsequent the “NO” response 214, the process is endedat a process step 222.

A process step 224 follows the process step 218, which involves thecopyright claiming module 220 executing numerous process steps. The user(e.g., the first user 102A) becomes the creator of the AI-generatedmultimedia and has a choice, at a process step 224, to claim a copyrightto the AI-generated multimedia. A process step 226 or a process step 228may follow the process step 224. The process step 226 is a “NO” responseto the process step 224. Subsequent the process step 226, the process isended at the process step 222.

The process step 228 is a “YES” response to the process step 224.Subsequent the process step 228, a process step 230 occurs, where thecopyright claiming module 220 verifies the AI-generated multimedia fororiginality. At this process step, the copyright claiming module 220checks the originality of the AI-generated multimedia by comparing theAI-generated multimedia to all works of the same type in a file andrecord storage 252. Moreover, at a process step 250, support informationmay be used from the file and record storage 252 to verify theoriginality at the process step 230. Such information may be written tothe blockchain for the copyright record. Moreover, the AI-generated workmultimedia work is stored to a decentralized file storage 260.

A process step 232 determines if the AI-generated work passes theprocess step 230. A “YES” response 240 or a “NO” response 234 may followthe process step 232. A process step 242 follows the “YES” response 240,where a true copyright for the AI-generated multimedia is stored. Then,the copyright for the AI-generated multimedia is transmitted at aprocess step 248 to the file and record storage module 252. The file andrecord storage 252 may include a decentralized file storage 260 and ablockchain 262. The associated copyright will be stored to theblockchain 262.

In response to the “NO” response 234, a process step 236 occurs wherethe user (e.g., the first user 102A) can choose if the user (e.g., thefirst user 102A) wishes to wait for a future update of the verificationpolicy by queueing the AI-generated multimedia or ending the process. A“YES” response 238 or a “NO” response 237 follows the process step 236.If the “NO” response 237 follows the process step 236, the process isended at the process step 222. A process step 246 follows the “YES”response 238, where the copyright claiming module 220 stores the pendingcopyright for the AI-generated work. Then, the copyright for theAI-generated work is transmitted, by the copyright claiming module 220,at the process step 248 to the file and record storage module 252.

The asset exchanging module 254 of FIG. 4 allows for the buying users(e.g., a buyer 364 of FIG. 8 and FIG. 9) to browse and filter allAI-generated multimedia. When the buyer 364 wishes to use theAI-generated multimedia, the first user 102A exchanges the copyright forthe AI-generated multimedia with the buyer 364 for a short time periodor transfers the copyright for the AI-generated multimedia to the buyer364 in exchange for the NFTs 134. This exchanging of information iswritten to the blockchain 262, while the file is not changed. Forexample, as shown in FIG. 4, an exchange occurs at a process step 256. Aprocess step 258 follows the process step 256 and includes exchangingthe copyright information for the AI-generated multimedia to the fileand record storage 252.

Thus, as described, the instant invention provides a method and systemfor automatically generating a copyright for an AI-generated multimedia.When one person/user uses a smart contract that allows AI modelexecution to generate data, the person is automatically given acopyright associated with the data. When the person decides to claim thecopyright, the system executes another smart contract to verify theoriginality of the content of the data in the whole blockchain database.It should be appreciated that once the smart contract 138 is executed,the payment is executed. This way of writing enhances the trust betweenparties and eliminates the intermediaries (such as a bank). As a result,the transparency of the system is improved.

If the work is new and original (defined by the work never having beenwritten on the blockchain before), the copyright is assigned to the userand immutably written to the blockchain, meaning the copyright will berecognized for future exchanging events. Otherwise, the data is storedon a queue and can be re-examined for the copyright when the systemupdates the originality verification policies. The copyrighted data canbe then exchanged in various forms: lending (for display, print,publish, vend, perform, adapt and/or reproduction) or transferring. Theadvantages of this method and system include: (1) providing an automatedand organized system of copyright issuing and (2) the decentralizednature of the blockchain technology makes it (i) fast in execution, (ii)trustful, and (iii) efficient, as well as openly accessible andtraceable. The governance of policies in the system can be done in adecentralized autonomous organization (DAO) manner or in a centralizedmanner.

Specifically, the DAO is an organization represented by rules encoded asa computer program that is transparent, controlled by the organizationmembers and not influenced by a central government. A DAO's financialtransaction record and program rules are maintained on a blockchain.Moreover, the DAOs are typified by the use of blockchain technology toprovide a secure digital ledger to track financial interactions acrossthe Internet. This approach eliminates the need to involve a mutuallyacceptable trusted third party in a financial transaction, thussimplifying the transaction. The costs of a blockchain-enabledtransaction and of the associated data reporting may be substantiallyoffset by the elimination of both the trusted third party and of theneed for repetitive recording of contract exchanges in differentrecords. In a centralized manner, the group of specific people willdecide if the proposal should be applied.

FIG. 5A depicts a system configured to create and exchange a copyrightfor each AI-generated multimedia via a blockchain, according to at leastsome embodiments disclosed herein.

Specifically, FIG. 5A utilizes multiple user interfaces for generatingmultimedia and exchanging the copyright for the multimedia. As describedherein, the user interfaces may equip users with functionalities to use,trade, destroy, upgrade, combine, rent, loan, or lose the generatedmultimedia.

The system of FIG. 5A includes a platform 264 and the first user 102A.The multiple interfaces of FIG. 5A include: a generation interface 294,a copyright interface 300, and a marketplace interface 301. The systemof FIG. 5A executes a method that begins at a process step 266. Wherethe first user 102A selects an AI model via the generation interface294. The first user 102A may also input a reference via the generationinterface 294 at a process step 270.

Then, a process step 268 occurs, where the AI platform (e.g., theplatform 264) is executed. Next, the AI-generated multimedia 216 iscreated or generated. Next, the copyright interface 300 prompts thefirst user 102A to claim property to the AI-generated multimedia 216 ata process step 278. If the first user 102A wishes to claim property tothe AI-generated multimedia 216 (e.g., follow a “YES” step 280), then,the copyright AI platform is executed at a process step 284. If thefirst user 102A does not wish to claim property to the AI-generatedmultimedia 216 (e.g., follow a “NO” step 282), then, the method ends.

Subsequent the process step 284, a process step 286 occurs where it isdetermined if the AI-generated multimedia passes an examination process.A “YES” response 290 or a “NO” response 288 follows the process step286. In response to the “YES” response 290, a process step 292 occurswhere the copyright for the AI-generated multimedia is written to theblockchain 262. The blockchain 262 and the decentralized storage (IPFS)260 are part of the file and record storage 252. In response to the “NO”response 288, the method is ended.

At the marketplace interface 301, transferring or lending of thecopyright for the AI-generated multimedia may occur at a process step194 between the first user 102A (e.g., the owner of the copyright forthe AI-generated multimedia) and another user/buyer. Such transferringof the copyright for the AI-generated multimedia may occur at a processstep 296 and such lending of the copyright for the AI-generatedmultimedia may occur at a process step 298. In response to thetransferring of the copyright for the AI-generated multimedia at theprocess step 296, ownership of the copyright for the AI-generatedmultimedia may be updated at a process step 302. Such update may bestored in the file and record storage 252. Moreover, in response to thelending of the copyright for the AI-generated multimedia at the processstep 298, limit access may be allowed to the copyright for theAI-generated multimedia at a process step 304. Such information/data maybe stored in the file and record storage 252.

FIG. 5B depicts another system configured to create and exchange acopyright for each AI-generated multimedia via a blockchain, accordingto at least some embodiments disclosed herein.

Specifically, FIG. 5B explains how one can adapt the mechanism describedherein to any editing platform (such as an image editing platform likePhotoshop, a video editing platform, or a music editing platform, etc.).If the platform is equipped with a monitoring AI model, one can verifythe origin of the generated multimedia and hence, can use the copyrightclaiming flow and the multimedia for exchanging purposes.

The system of FIG. 5B includes the platform 264 and multiple interfaces,such as the generation interface 294, the copyright interface 300, andthe marketplace interface 301. A method of FIG. 5B begins at a processstep 386, where the first user 102A connects to the wallet via thegeneration interface 294. Next, a process step 388 follows the processstep 386 and includes executing an editing platform. Then, a processstep 390 follows the process step 388 and includes generating themultimedia. In some embodiments, a process step 292 occurs inconjunction with the process step 388 and includes executing themonitoring AI platform.

Subsequent the process step 390, a process step 394 occurs, where thecopyright interface 300 prompts the first user 102A to claim property tothe AI-generated multimedia 216. A “YES” response 398 or a “NO” response396 follows the process step 394. Subsequent the “NO” response 396, themethod is ended. Following the “YES” response 398, a process step 400occurs where the copyright AI platform is executed.

Subsequent the process step 400, a process step 402 occurs where it isdetermined if the AI-generated multimedia passes an examination process.A “YES” response 406 or a “NO” response 404 follows the process step402. Subsequent the “NO” response 404, the method is ended. Subsequentthe “YES” response 406, a process step 408 occurs where the copyrightfor the AI-generated multimedia is written to the blockchain 262. Theblockchain 262 and the decentralized storage (IPFS) 260 are parts of thefile and record storage 252.

Moreover, at the marketplace interface 301, transferring or lending ofthe copyright for the AI-generated multimedia may occur at a processstep 410 between the first user 102A (e.g., the owner of the copyrightfor the AI-generated multimedia) and another user/buyer. Suchtransferring of the copyright for the AI-generated multimedia may occurat a process step 412 and such lending of the copyright for theAI-generated multimedia may occur at a process step 414. In response tothe transferring of the copyright for the AI-generated multimedia at theprocess step 412, ownership of the copyright for the AI-generatedmultimedia may be updated at a process step 416. Such update may bestored in the file and record storage 252. Moreover, in response to thelending of the copyright for the AI-generated multimedia at the processstep 414, limit access may be allowed to the copyright for theAI-generated multimedia at a process step 418. Such information/data maybe stored in the file and record storage 252.

FIG. 6 depicts a schematic diagram of originality verification executedby a system, according to at least some embodiments disclosed herein.

As shown in FIG. 6, first data 146A and second data 146B associated withthe AI-generated multimedia may be verified via a verification policy312 at a process step 306. The first data 146A and the second data 146Bmay be of the same kind (such as text, graphics, animation, video,sound, etc.). The verification policy 312 of the process step 306 may beselected by the first user 102A. Further, the verification policy 312 ofthe AI-generated multimedia is based on pair-comparison between two dataobjects of the same type. In examples, the verification policy 312 maybe based on an AI model 314, crowd voting 316, a human validator 318,and/or blind voting 320.

Specifically, the AI model 314 for comparison takes two objects as inputand calculates the similarity between the two inputs. The crowd voting316 utilizes multiple humans (e.g., a crowd) on the Internet to performa voting (either binary, or ordinal scale scores like 1 to 5 or 1 to10). The human validator 318 is an expert for verification. Moreover,the blind voting 320 involves comparing two objects without knowing thedetails or history of them. Further, aspects/components of theverification policy 312 may be aggregated from multiple sources at a.process step 322. The aggregation at the process step 322 returns asimilarity score at a process step 308 and a threshold-based decision ata process step 310.

It should be appreciated that, as described herein, the verificationpolicy 312 is subject to change based on changes to copyright law,algorithmic changes to compute the originality, algorithmic changes tocompute the legal clauses, and/or changes of purpose of the module. Theverification policy 312 is made in the DAO or the centralized manner.

FIG. 7 depicts a schematic diagram showcasing a mechanism for conflicthandling with legal checking executed by a system, according to at leastsome embodiments disclosed herein.

As shown in FIG. 7, at a process step 324, a record identifierassociated with an AI-generated model is selected. Next, a process step326, a storage is scanned. Then, a process step 328 occurs, whererelated data (such as an input, an output, or the copyright for theAI-generated multimedia) is identified. Next, at a process step 330, alegal component is analyzed. The legal component is a computer programthat computes the legal correctness according to current regulations,copyright laws, etc. to return a legal decision. A legal decision isidentified at a process step 332. Depending on the decision at theprocess step 332, the corresponding copyright information for theAI-generated multimedia can be updated (either it's “okay”, “arguable”or “removable”) at a process step 334. Subsequent the process step 334,various information is stored in the file and record storage 338. Next,the process of FIG. 7 ends.

FIG. 8 depicts a schematic diagram showcasing currency flow whencreating AI-generated multimedia, according to at least some embodimentsdisclosed herein. FIG. 9 depicts a schematic diagram of currency flowwhen exchanging AI-generated multimedia, according to at least someembodiments disclosed herein.

A creator (e.g., the first user 102A who uses service to generate theAI-generated multimedia) pays a fee at a process step 340 to generatethe AI-generated multimedia at a process step 350. The creator also paysfees at a process step 352 to claim the copyright (at a process step354) or pays a fee at a process step 356 to wait for a policy update (ata process step 360). Such information may be saved in a system wallet358. The system wallet 358 allows for the balancing of fees/paymentsbetween the various parties.

Moreover, the AI programmer 346 and/or the AI API provider 348 mayreceive a royalty payment at a process step 342 and a process step 368.More specifically, each time the AI-generated multimedia is created, theAI programmer 346 and/or the AI API provider 348 receive a share of thefee (e.g., a royalty). The percentage can be set via the smart contractby the AI programmer 346 and/or the AI API provider 348. It should beappreciated that the information may be exchanged at a process step 362with the buyer 364.

When the buyer 364 wants permission to use/access a copyright of a work(e.g., the AI-generated multimedia) for a limited-time (e.g., displayingor replaying), or if the AI-generated multimedia is transferred, thebuyer 364 pays a cost 366, and the creator (e.g., the first user 102A),the AI programmer 346, and/or the AI API provider 348 receive royaltypayments. Percentages of payment are defined between the involvedparties. In some implementations, a split of the royalty percentages iseven between the creator (e.g., the first user 102A), the AI programmer346, and/or the AI API provider 348. In other examples, the split of theroyalty percentages is even between the creator (e.g., the first user102A), the AI programmer 346, and the AI APT provider 348.

As an illustrative example, the AI programmer 346 may receive 50% of theroyalty payment from the buyer 364, the AI API provider 348 may receive30% from the royalty payment from the buyer 364, and the creator (e,g.,the first user 102A) may receive 20% of the royalty payment from thebuyer 364. A remainder of the royalty payment may be used to run thesystem. Additionally, in some implementations, the creator (e.g., thefirst user 102A) may check the royalty terms prior to selecting the AImodel that is used to generate the AI-generated multimedia.

In some implementations of the system described herein, there is apossibility that the creator (e.g., the first user 102A who uses the AIto generate the AI-generated multimedia) wants or is required to editthe AI-generated multimedia. This can happen at several stages of thesystem: (1) after generating the AI-generated multimedia and/or (2) whenclaiming for the copyright for the AI-generated multimedia. TheAI-generated multimedia may be edited in the following way: (1) hiding asecret message into the AT-generated multimedia or metadata or (2)setting a unique identification into the AI-generated multimedia. Theedit to the AT-generated multimedia poses little or no change to anappearance of the AI-generated multimedia.

In other implementations of the system described herein, the creator(e.g., the first user 102A who uses the AI to generate the AI-generatedmultimedia) may name the AI-generated multimedia in any way the creatorwishes (e.g., giving the AI-generated multimedia a title). This mayhappen (1) at the time the copyright is claimed for the AI-generatedmultimedia or (2) when browsing AI-generated multimedia and deciding togive each asset a title. In this second scenario, the creator pay anadditional fee for updating information in the blockchain.

In further implementations of the system, one person can make anadaptation (e.g., make a modification or a change) by using the AI modelor by manually processing the AI-generated multimedia to create a newAI-generated multimedia. The person may claim the copyright to the newAI-generated multimedia as long as it passes all copyright-relatedpolicies of the system described herein.

FIG. 10 depicts a block diagram depicting how a system provides anAppstore for applications, according to at least some embodimentsdisclosed herein.

The system of FIG. 10 provides access to an Appstore for applications.Components of FIG. 10 include the first user 102A, the walletapplications 132, an Appstore 362, an app repository database 364, anapp/update security service 366, an app compliance service 368, a ledgerblockchain database 360, and/or one or more servers 128. The first user102A can navigate the Appstore 362 to identify and download applicationsfrom the app repository database 364. These applications may include thewallet applications 132, for example.

FIG. 11 depicts a block diagram of a server which may be used by asystem, according to at least some embodiments disclosed herein.

A block diagram of FIG. 11 illustrates a server 128. One or more servers128 may be used in the system or may be standalone servers. The server128 may be a digital computer that, in terms of hardware architecture,generally includes a processor 370, input/output (I/O) interfaces 372, anetwork interface 374, the datastore 108, and memory 376.

The components (108, 370, 372, 374, and 376) are communicatively coupledvia a local interface 384. The local interface 384 may be, for examplebut not limited to, one or more buses or other wired or wirelessconnections. The local interface 384 may have additional elements, whichare omitted for simplicity, such as controllers, buffers, drivers,repeaters, and receivers, etc., to enable communications. Further, thelocal interface 384 may include address, control, and/or dataconnections to enable communications among the aforementionedcomponents.

The processor 370 is a hardware device for executing softwareinstructions. The processor 370 may be any custom made or commerciallyavailable processor, a central processing unit (CPU), an auxiliaryprocessor among several processors associated with the server 128, asemiconductor-based microprocessor, or generally any device forexecuting software instructions. When the server 128 is in operation,the processor 370 is configured to execute software stored within thememory 376, to communicate data to and from the memory 376, and togenerally control operations of the server 128 pursuant to the softwareinstructions.

The I/O interfaces 372 may be used to receive user input from and/or forproviding system output to one or more devices or components. User inputmay be provided via, for example, a keyboard, touch pad, and/or a mouse.System output may be provided via a display device and a printer (notshown). The I/O interfaces 372 may include, for example, a serial port,a parallel port, a small computer system interface (SCSI), a serial ATA(SATA), a fiber channel, Infinihand, iSCSI, a PCI Express interface(PCI-x), an infrared (IR) interface, a radio frequency (RF) interface,and/or a universal serial bus (USB) interface.

The network interface 374 may be used to enable the server 128 tocommunicate on a network, such as the Internet, a wide area network(WAN), a local area network (LAN), etc. The network interface 374 mayinclude an Ethernet card or adapter or a wireless local area network (WLAN) card or adapter. The network interface 374 may include address,control, and/or data connections to enable communications on thenetwork.

The datastore 108 may be used to store data. The datastore 108 mayinclude any of volatile memory elements (e.g., random access memory(RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memoryelements (e.g., ROM, hard drive, tape, CDROM, and the like), andcombinations thereof. Moreover, the datastore 108 may incorporateelectronic, magnetic, optical, and/or other types of storage media. Inone example, the datastore 108 may be located internal to the server 128such as, an internal hard drive connected to the local interface 384 inthe server 128. In other examples, the datastore 108 may be locatedexternal to the server 128 such as, an external hard drive connected tothe I/O interfaces 372 (e.g., SCSI or USB connection). In a furtherembodiment, the datastore 108 may be connected to the server 128 througha network, such as, a network attached file server.

The memory 376 may include any of volatile memory elements (e.g., randomaccess memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatilememory elements (e.g., ROM, hard drive, tape, CDROM, etc.), andcombinations thereof. Moreover, the memory 376 may incorporateelectronic, magnetic, optical, and/or other types of storage media. Notethat the memory 376 may have a distributed architecture, where variouscomponents are situated remotely from one another, but can be accessedby the processor 370. The software in memory 376 may include one or moresoftware programs, each of which includes an ordered listing ofexecutable instructions for implementing logical functions.

The software in the memory 376 includes a suitable operating system(O/S) 378 and one or more programs 380. The operating system 378controls the execution of other computer programs, such as the one ormore programs 380, and provides scheduling, input-output control, fileand data management, memory management, and communication control andrelated services. The one or more programs 380 may be configured toimplement the various processes and methods described herein.

FIG. 12 depicts a block diagram of a client device which may be used bya system, according to at least some embodiments disclosed herein.

The block diagram of FIG. 12 illustrates the first client device 104A,which may be used in the system described herein. The first clientdevice 104A may be a digital device that includes a processor 370,input/output (I/O) interfaces 372, a radio 382, the datastore 108, andmemory 376. The components (370, 372, 382, 108, and 376) arecommunicatively coupled via a local interface 384. The local interface384 can be one or more buses or other wired or wireless connections, asis known in the art. The local interface 384 can have additionalelements, such as controllers, buffers, drivers, etc., to enablecommunications.

The processor 370 is a hardware device for executing softwareinstructions. The processor 370 can be any custom-made or commerciallyavailable processor, the CPU, or an auxiliary processor associated withthe first client device 104A, a semiconductor-based microprocessor, orany device for executing software instructions. When the first clientdevice 104A is in operation, the processor 370 is configured to executesoftware stored within the memory 376, to communicate data to and fromthe memory 376, and to generally control operations of the first clientdevice 104A based on the software instructions.

Moreover, the processor 370 may include a mobile optimized processoroptimized for power consumption and mobile applications. The I/Ointerfaces 372 can be used to receive user input from and/or forproviding system output. User input can be provided via, for example, akeypad, a touch screen, a scroll ball, a scroll bar, buttons, etc.System output can be provided via a display device such as a liquidcrystal display (LCD), touch screen, etc. The I/O interfaces 372 canalso include a serial port, a parallel port, a small computer systeminterface (SCSI), an infrared (IR) interface, a radio frequency (RF)interface, a universal serial bus (USB) interface, etc. Further, the I/Ointerfaces 372 can include a graphical user interface (GUI) that enablesa user to interact with the first client device 104A. Additionally, theI/O interfaces 372 may further include an imaging device (such as acamera),

The radio 382 enables wireless communication to an external accessdevice or network. Any number of suitable wireless data communicationprotocols, techniques, or methodologies can be supported by the radio382, including, without limitation: RF; IrDA (infrared); Bluetooth;ZigBee (and other variants of the IEEE 802.15 protocol); IEEE 802.11(any variation); IEEE 802.16 (WiMAX or any other variation); DirectSequence Spread Spectrum; Frequency Hopping Spread Spectrum; Long TermEvolution (LIE); cellular/wireless/cordless telecommunication protocols(e.g. 3G/4G/5G, etc.); wireless home network communication protocols;paging network protocols; magnetic induction; satellite datacommunication protocols; wireless hospital or health care facilitynetwork protocols such as those operating in the WMTS bands; GPRS;proprietary wireless data communication protocols such as variants ofWireless USB; and any other protocols for wireless communication.

The datastore 108 may be used to store data. The datastore 108 mayinclude any of volatile memory elements, nonvolatile memory elements,and combinations thereof. Moreover, the datastore 108 may incorporateelectronic, magnetic, optical, and/or other types of storage media. Thememory 376 may include any of volatile memory elements, and/ornonvolatile memory elements. Further, the memory 376 may incorporateelectronic, magnetic, optical, and/or other types of storage media. Insome examples, the memory 376 may have a distributed architecture, wherevarious components are situated remotely from one another, but can beaccessed by the processor 370.

The software in the memory 376 can include one or more softwareprograms, each of which includes an ordered listing of executableinstructions for implementing functions. In the example of FIG. 12, thesoftware in the memory 376 includes a suitable operating system (O/S)378 and programs 380. The operating system 378 controls the execution ofother computer programs. The programs 380 may include variousapplications configured to provide end user functionality with the firstclient device 104A. For example, the programs 380 may include: a webbrowser, social networking applications, streaming media applications,games, etc. In an example, an end user (e.g., the first user 102A) usesone or more of the programs 380 with a network to manipulate informationof the system.

The descriptions of the various embodiments of the present inventionhave beery presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers or ordinary skill in the art to understand the embodimentsdisclosed herein.

When introducing elements of the present disclosure or the embodimentsthereof, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. Similarly, the adjective“another,” when used to introduce an element, is intended to mean one ormore elements. The terms “including” and “having” are intended to beinclusive such that there may be additional elements other than thelisted elements.

Although this invention has been described with a certain degree ofparticularity, it is to be understood that the present disclosure hasbeen made only by way of illustration and that numerous changes in thedetails of construction and arrangement of parts may be resorted towithout departing from the spirit and the scope of the invention.

What is claimed is:
 1. A method executed by a system for creating andexchanging a copyright for each artificial intelligence (AI)-generatedmultimedia, the method comprising: receiving, via a multimediageneration module of the system, a user selection of an AI model for amultimedia; receiving, via the multimedia, generation module, areference input for the multimedia from the user; in response to adetermination that the reference input complies with system policies,generating, via the multimedia generation module, an AI-generatedmultimedia from the reference input using the AI model; receiving, via acopyright claiming module of the system, a notification from the userthat the user wants to claim a copyright in the AI-generated multimedia;comparing, via the copyright claiming module, the AI-generatedmultimedia against works of a same type in a blockchain anddecentralized file storage; in response to a determination that theAI-generated multimedia fails to match the works of the same type in theblockchain and decentralized file storage, identifying, via thecopyright claiming module, the AI-generated multimedia as havingoriginality; executing, via the copyright claiming module, averification policy selected by the user to verify the originality ofthe AI-generated multimedia; storing, via the copyright claiming module,the copyright for the AI-generated multimedia and the AI-generatedmultimedia in the blockchain and decentralized file storage; receiving,via an asset exchanging module of the system, a request from a buyer touse the copyright for the AI-generated multimedia; prompting, via theasset exchanging module, the user to exchange the copyright for theAI-generated multimedia with the buyer for a payment; facilitating, viathe asset exchanging module, the exchange between the user and thebuyer; and writing, via the asset exchanging module, the exchange to ablockchain.
 2. The method of claim 1, wherein, in response to adetermination that the AI-generated multimedia matches the works of thesame type in the blockchain and decentralized file storage, the methodfurther comprises: identifying, via the copyright claiming module, theAI-generated multimedia as lacking the originality; receiving, via thecopyright claiming module, a notification from the user that the userwants wait for a future update of the verification policy by queueingthe AI-generated multimedia; and storing, via the copyright claimingmodule, a pending copyright for the AI-generated multimedia in theblockchain and decentralized file storage;
 3. The method of claim 1,wherein the verification policy is executed in a decentralizedautonomous organization (DAO) manner.
 4. The method of claim 1, whereinthe verification policy is executed in a centralized manner.
 5. Themethod of claim 1, wherein the verification policy of the AI-generatedmultimedia is based on a pair-comparison between two data objects of asame type,
 6. The method of claim 5, wherein the verification policy isbased on the AI model that compares the two data objects as inputs andcalculates a similarity score between the two inputs.
 7. The method ofclaim 5, wherein the verification policy is based on crowd voting thatcomprises: receiving, from each user of a group of users, a scoreassociated with the pair-comparison; aggregating the scores; andcalculating an average score from the aggregated scores.
 8. The methodof claim 5, wherein the verification policy is based on a humanvalidator that supplies a score associated with the pair-comparison. 9.The method of claim 5, wherein the verification policy is based on blindvoting where one or more users provide a score for the pair-comparisonwithout having knowledge of a history between the two data objects. 10.The method of claim 1, further comprising: updating, via the copyrightclaiming module, the verification policy based on at least one of:changes to a copyright law, algorithmic changes that compute theoriginality of the AI-generated multimedia, algorithmic changes thatcompute legal clauses associated with the copyright law, and changes toa purpose of the copyright claiming module.
 11. The method of claim 1,further comprising: executing a method for conflict handling via thecopyright claiming module.
 12. The method of claim 11, wherein themethod for conflict handling comprises: receiving, from the user, arecord identifier associated with the model; scanning the blockchain anddecentralized file storage; identifying related data for theAL-generated multimedia; and analyzing a legal component to identify alegal decision.
 13. The method of claim 12, wherein the related data forthe AI-generated multimedia is selected from the group consisting of: aninput, an output, and the copyright.
 14. The method of claim 12, whereinthe legal component comprises a computer program that computes a legalcorrectness according to at least one current regulation or copyrightlaw.
 15. The method of claim 12, further comprising: determining thatthe legal decision complies with the at least one current regulation orcopyright law; updating the AI-generated multimedia to reflect thecompliance; and updating the blockchain and decentralized file storagewith the compliance.
 16. The method of claim 12, further comprising:determining that the legal decision fails to comply with the at leastone current regulation or copyright law; updating the AI-generatedmultimedia to reflect a lack of compliance; and updating the blockchainand decentralized file storage with the lack of compliance.
 17. A systemconfigured to execute a method for creating and exchanging a copyrightfor each artificial intelligence (AO-generated multimedia, the systemcomprising: a multimedia generation module configured to: receive a userselection of an AI model for a multimedia; receive a reference input forthe multimedia from the user; and in response to a determination thatthe reference input complies with system policies, generate anAI-generated multimedia from the reference input using the AI model; acopyright claiming module configured to: receive an indication from theuser that the user wants to claim a copyright in the AI-generatedmultimedia; compare the AI-generated multimedia against works of a sametype in a blockchain and decentralized file storage; in response to adetermination that the AI-generated multimedia fails to match the worksof the same type in the blockchain and decentralized file storage,identify the AI-generated multimedia as having originality; executing averification policy selected by the user in a decentralized autonomousorganization (DAO) manner to verify the originality of the AI-generatedmultimedia, wherein the verification policy is based on the AI model,crowd voting, a human validator, or blind voting; updating theverification policy based on at least one of: changes to a copyrightlaw, algorithmic changes that compute the originality of theAI-generated multimedia, algorithmic changes that compute legal clausesassociated with the copyright law, and changes to a purpose of thecopyright claiming module; re-executing the updated verification policy;store the copyright for the AI-generated multimedia and the AI-generatedmultimedia in the blockchain and decentralized file storage; and anasset exchanging module configured to: receive a request from a buyer touse the copyright for the AI-generated multimedia: prompt the user toexchange the copyright for the AI-generated multimedia with the buyerfor cryptocurrency; facilitate the exchange between the user and thebuyer; and write the exchange to a blockchain.
 18. The system of claim17, wherein the copyright claiming module is further configured toexecute a method for conflict handling.
 19. The system of claim 18,wherein the method for conflict handling comprises: receiving, from theuser, a record identifier associated with the model; scanning theblockchain and decentralized file storage; identifying related data forthe AI-generated multimedia, wherein the related data for theAI-generated multimedia is selected from the group consisting of: aninput, an output, and the copyright; and analyzing a legal component toidentify a legal decision, wherein the legal component comprises acomputer program that computes a legal correctness according to at leastone current regulation or copyright law.
 20. The system of claim 19,wherein the method for conflict handling further comprises: determiningif the legal decision complies with the at least one current regulationor copyright law; updating the AI-generated multimedia to reflect thecompliance or lack of compliance; and updating the blockchain anddecentralized file storage with the compliance or the lack ofcompliance.